Overview

Dataset statistics

Number of variables15
Number of observations1232
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory134.8 KiB
Average record size in memory112.0 B

Variable types

Categorical3
Numeric12

Alerts

Year is highly overall correlated with SLGHigh correlation
RS is highly overall correlated with W and 3 other fieldsHigh correlation
RA is highly overall correlated with WHigh correlation
W is highly overall correlated with RS and 4 other fieldsHigh correlation
OBP is highly overall correlated with RS and 2 other fieldsHigh correlation
SLG is highly overall correlated with Year and 3 other fieldsHigh correlation
BA is highly overall correlated with RS and 2 other fieldsHigh correlation
RankSeason is highly overall correlated with W and 2 other fieldsHigh correlation
RankPlayoffs is highly overall correlated with W and 2 other fieldsHigh correlation
OOBP is highly overall correlated with OSLGHigh correlation
OSLG is highly overall correlated with OOBPHigh correlation
Team is highly overall correlated with LeagueHigh correlation
League is highly overall correlated with TeamHigh correlation
Playoffs is highly overall correlated with W and 2 other fieldsHigh correlation
League is uniformly distributedUniform
RankSeason has 988 (80.2%) zerosZeros
RankPlayoffs has 988 (80.2%) zerosZeros

Reproduction

Analysis started2023-06-07 08:00:41.457399
Analysis finished2023-06-07 08:01:06.598393
Duration25.14 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

Team
Categorical

Distinct39
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size19.2 KiB
HOU
 
47
DET
 
47
BAL
 
47
BOS
 
47
CHC
 
47
Other values (34)
997 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3696
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st rowARI
2nd rowATL
3rd rowBAL
4th rowBOS
5th rowCHC

Common Values

ValueCountFrequency (%)
HOU 47
 
3.8%
DET 47
 
3.8%
BAL 47
 
3.8%
BOS 47
 
3.8%
CHC 47
 
3.8%
CHW 47
 
3.8%
CIN 47
 
3.8%
CLE 47
 
3.8%
STL 47
 
3.8%
PHI 47
 
3.8%
Other values (29) 762
61.9%

Length

2023-06-07T11:01:06.664183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hou 47
 
3.8%
det 47
 
3.8%
nyy 47
 
3.8%
nym 47
 
3.8%
min 47
 
3.8%
lad 47
 
3.8%
sfg 47
 
3.8%
pit 47
 
3.8%
phi 47
 
3.8%
stl 47
 
3.8%
Other values (29) 762
61.9%

Most occurring characters

ValueCountFrequency (%)
L 348
 
9.4%
C 327
 
8.8%
A 326
 
8.8%
T 269
 
7.3%
I 243
 
6.6%
N 240
 
6.5%
S 233
 
6.3%
O 218
 
5.9%
H 188
 
5.1%
M 170
 
4.6%
Other values (12) 1134
30.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3696
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 348
 
9.4%
C 327
 
8.8%
A 326
 
8.8%
T 269
 
7.3%
I 243
 
6.6%
N 240
 
6.5%
S 233
 
6.3%
O 218
 
5.9%
H 188
 
5.1%
M 170
 
4.6%
Other values (12) 1134
30.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 3696
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 348
 
9.4%
C 327
 
8.8%
A 326
 
8.8%
T 269
 
7.3%
I 243
 
6.6%
N 240
 
6.5%
S 233
 
6.3%
O 218
 
5.9%
H 188
 
5.1%
M 170
 
4.6%
Other values (12) 1134
30.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 348
 
9.4%
C 327
 
8.8%
A 326
 
8.8%
T 269
 
7.3%
I 243
 
6.6%
N 240
 
6.5%
S 233
 
6.3%
O 218
 
5.9%
H 188
 
5.1%
M 170
 
4.6%
Other values (12) 1134
30.7%

League
Categorical

HIGH CORRELATION  UNIFORM 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size19.2 KiB
NL
616 
AL
616 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2464
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNL
2nd rowNL
3rd rowAL
4th rowAL
5th rowNL

Common Values

ValueCountFrequency (%)
NL 616
50.0%
AL 616
50.0%

Length

2023-06-07T11:01:06.804133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-07T11:01:06.940563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nl 616
50.0%
al 616
50.0%

Most occurring characters

ValueCountFrequency (%)
L 1232
50.0%
N 616
25.0%
A 616
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2464
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 1232
50.0%
N 616
25.0%
A 616
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2464
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 1232
50.0%
N 616
25.0%
A 616
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2464
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 1232
50.0%
N 616
25.0%
A 616
25.0%

Year
Real number (ℝ)

Distinct47
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1988.9578
Minimum1962
Maximum2012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.4 KiB
2023-06-07T11:01:07.068885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1962
5-th percentile1965
Q11976.75
median1989
Q32002
95-th percentile2010
Maximum2012
Range50
Interquartile range (IQR)25.25

Descriptive statistics

Standard deviation14.819625
Coefficient of variation (CV)0.00745095
Kurtosis-1.2048462
Mean1988.9578
Median Absolute Deviation (MAD)13
Skewness-0.15192926
Sum2450396
Variance219.62129
MonotonicityDecreasing
2023-06-07T11:01:07.237363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
2012 30
 
2.4%
2004 30
 
2.4%
2011 30
 
2.4%
1998 30
 
2.4%
1999 30
 
2.4%
2001 30
 
2.4%
2002 30
 
2.4%
2003 30
 
2.4%
2000 30
 
2.4%
2005 30
 
2.4%
Other values (37) 932
75.6%
ValueCountFrequency (%)
1962 20
1.6%
1963 20
1.6%
1964 20
1.6%
1965 20
1.6%
1966 20
1.6%
1967 20
1.6%
1968 20
1.6%
1969 24
1.9%
1970 24
1.9%
1971 24
1.9%
ValueCountFrequency (%)
2012 30
2.4%
2011 30
2.4%
2010 30
2.4%
2009 30
2.4%
2008 30
2.4%
2007 30
2.4%
2006 30
2.4%
2005 30
2.4%
2004 30
2.4%
2003 30
2.4%

RS
Real number (ℝ)

Distinct374
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean715.08198
Minimum463
Maximum1009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.2 KiB
2023-06-07T11:01:07.419050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum463
5-th percentile570.55
Q1652
median711
Q3775
95-th percentile871.45
Maximum1009
Range546
Interquartile range (IQR)123

Descriptive statistics

Standard deviation91.534294
Coefficient of variation (CV)0.12800531
Kurtosis-0.020576521
Mean715.08198
Median Absolute Deviation (MAD)61
Skewness0.17450786
Sum880981
Variance8378.527
MonotonicityNot monotonic
2023-06-07T11:01:07.584393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
682 11
 
0.9%
691 11
 
0.9%
707 10
 
0.8%
735 10
 
0.8%
758 10
 
0.8%
708 10
 
0.8%
673 9
 
0.7%
714 9
 
0.7%
654 9
 
0.7%
731 9
 
0.7%
Other values (364) 1134
92.0%
ValueCountFrequency (%)
463 1
0.1%
464 1
0.1%
468 1
0.1%
470 1
0.1%
473 1
0.1%
486 1
0.1%
495 2
0.2%
498 2
0.2%
501 1
0.1%
510 1
0.1%
ValueCountFrequency (%)
1009 1
0.1%
993 1
0.1%
978 1
0.1%
968 2
0.2%
965 1
0.1%
961 2
0.2%
952 1
0.1%
950 1
0.1%
949 2
0.2%
947 1
0.1%

RA
Real number (ℝ)

Distinct381
Distinct (%)30.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean715.08198
Minimum472
Maximum1103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.2 KiB
2023-06-07T11:01:07.759832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum472
5-th percentile571.1
Q1649.75
median709
Q3774.25
95-th percentile877.8
Maximum1103
Range631
Interquartile range (IQR)124.5

Descriptive statistics

Standard deviation93.079933
Coefficient of variation (CV)0.1301668
Kurtosis-0.010927035
Mean715.08198
Median Absolute Deviation (MAD)62
Skewness0.29856263
Sum880981
Variance8663.8739
MonotonicityNot monotonic
2023-06-07T11:01:07.923807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
717 11
 
0.9%
657 10
 
0.8%
744 10
 
0.8%
731 10
 
0.8%
648 10
 
0.8%
680 10
 
0.8%
611 9
 
0.7%
643 9
 
0.7%
698 9
 
0.7%
745 9
 
0.7%
Other values (371) 1135
92.1%
ValueCountFrequency (%)
472 1
0.1%
490 1
0.1%
491 1
0.1%
492 1
0.1%
497 1
0.1%
499 1
0.1%
501 1
0.1%
504 1
0.1%
509 1
0.1%
517 2
0.2%
ValueCountFrequency (%)
1103 1
0.1%
1028 1
0.1%
974 1
0.1%
971 1
0.1%
969 1
0.1%
968 1
0.1%
967 2
0.2%
964 1
0.1%
957 1
0.1%
948 1
0.1%

W
Real number (ℝ)

Distinct63
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.904221
Minimum40
Maximum116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.2 KiB
2023-06-07T11:01:08.101177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile62
Q173
median81
Q389
95-th percentile98
Maximum116
Range76
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.458139
Coefficient of variation (CV)0.14162597
Kurtosis-0.29902183
Mean80.904221
Median Absolute Deviation (MAD)8
Skewness-0.18186642
Sum99674
Variance131.28895
MonotonicityNot monotonic
2023-06-07T11:01:08.275951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83 49
 
4.0%
86 46
 
3.7%
76 43
 
3.5%
79 43
 
3.5%
88 43
 
3.5%
90 40
 
3.2%
89 39
 
3.2%
75 39
 
3.2%
85 38
 
3.1%
80 37
 
3.0%
Other values (53) 815
66.2%
ValueCountFrequency (%)
40 1
 
0.1%
43 1
 
0.1%
50 1
 
0.1%
51 2
 
0.2%
52 2
 
0.2%
53 3
0.2%
54 5
0.4%
55 4
0.3%
56 6
0.5%
57 6
0.5%
ValueCountFrequency (%)
116 1
 
0.1%
114 1
 
0.1%
109 1
 
0.1%
108 3
 
0.2%
106 1
 
0.1%
105 1
 
0.1%
104 4
 
0.3%
103 9
0.7%
102 10
0.8%
101 11
0.9%

OBP
Real number (ℝ)

Distinct87
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32633117
Minimum0.277
Maximum0.373
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.2 KiB
2023-06-07T11:01:08.449618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.277
5-th percentile0.302
Q10.317
median0.326
Q30.337
95-th percentile0.352
Maximum0.373
Range0.096
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.015012772
Coefficient of variation (CV)0.046004715
Kurtosis0.067597287
Mean0.32633117
Median Absolute Deviation (MAD)0.01
Skewness0.017635262
Sum402.04
Variance0.00022538333
MonotonicityNot monotonic
2023-06-07T11:01:08.609779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.322 42
 
3.4%
0.32 39
 
3.2%
0.325 37
 
3.0%
0.321 36
 
2.9%
0.333 35
 
2.8%
0.324 34
 
2.8%
0.33 34
 
2.8%
0.331 33
 
2.7%
0.323 33
 
2.7%
0.339 31
 
2.5%
Other values (77) 878
71.3%
ValueCountFrequency (%)
0.277 1
 
0.1%
0.281 1
 
0.1%
0.283 1
 
0.1%
0.284 1
 
0.1%
0.285 3
0.2%
0.287 1
 
0.1%
0.288 2
0.2%
0.289 1
 
0.1%
0.29 1
 
0.1%
0.291 3
0.2%
ValueCountFrequency (%)
0.373 1
 
0.1%
0.369 1
 
0.1%
0.367 1
 
0.1%
0.366 3
0.2%
0.364 1
 
0.1%
0.363 2
 
0.2%
0.362 6
0.5%
0.361 3
0.2%
0.36 6
0.5%
0.359 1
 
0.1%

SLG
Real number (ℝ)

Distinct162
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.39734172
Minimum0.301
Maximum0.491
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.2 KiB
2023-06-07T11:01:08.781417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.301
5-th percentile0.344
Q10.375
median0.396
Q30.421
95-th percentile0.455
Maximum0.491
Range0.19
Interquartile range (IQR)0.046

Descriptive statistics

Standard deviation0.033266899
Coefficient of variation (CV)0.083723649
Kurtosis-0.31717178
Mean0.39734172
Median Absolute Deviation (MAD)0.023
Skewness0.054330043
Sum489.525
Variance0.0011066865
MonotonicityNot monotonic
2023-06-07T11:01:08.961475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.401 21
 
1.7%
0.395 20
 
1.6%
0.381 20
 
1.6%
0.409 19
 
1.5%
0.391 19
 
1.5%
0.388 18
 
1.5%
0.403 18
 
1.5%
0.396 18
 
1.5%
0.387 17
 
1.4%
0.385 17
 
1.4%
Other values (152) 1045
84.8%
ValueCountFrequency (%)
0.301 1
 
0.1%
0.311 1
 
0.1%
0.315 3
0.2%
0.317 2
0.2%
0.318 3
0.2%
0.319 2
0.2%
0.32 1
 
0.1%
0.325 1
 
0.1%
0.326 2
0.2%
0.327 3
0.2%
ValueCountFrequency (%)
0.491 1
 
0.1%
0.485 1
 
0.1%
0.484 1
 
0.1%
0.483 1
 
0.1%
0.479 1
 
0.1%
0.478 2
 
0.2%
0.477 1
 
0.1%
0.475 2
 
0.2%
0.472 6
0.5%
0.471 1
 
0.1%

BA
Real number (ℝ)

Distinct75
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25927273
Minimum0.214
Maximum0.294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.2 KiB
2023-06-07T11:01:09.154639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.214
5-th percentile0.238
Q10.251
median0.26
Q30.268
95-th percentile0.28
Maximum0.294
Range0.08
Interquartile range (IQR)0.017

Descriptive statistics

Standard deviation0.012907229
Coefficient of variation (CV)0.04978244
Kurtosis0.0095668343
Mean0.25927273
Median Absolute Deviation (MAD)0.009
Skewness-0.11118461
Sum319.424
Variance0.00016659656
MonotonicityNot monotonic
2023-06-07T11:01:09.349657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.263 49
 
4.0%
0.261 44
 
3.6%
0.258 41
 
3.3%
0.264 40
 
3.2%
0.256 39
 
3.2%
0.26 38
 
3.1%
0.259 36
 
2.9%
0.267 36
 
2.9%
0.262 36
 
2.9%
0.27 35
 
2.8%
Other values (65) 838
68.0%
ValueCountFrequency (%)
0.214 1
 
0.1%
0.219 1
 
0.1%
0.22 1
 
0.1%
0.221 1
 
0.1%
0.223 1
 
0.1%
0.224 1
 
0.1%
0.225 4
0.3%
0.227 2
0.2%
0.228 3
0.2%
0.229 3
0.2%
ValueCountFrequency (%)
0.294 1
 
0.1%
0.293 2
 
0.2%
0.292 1
 
0.1%
0.291 2
 
0.2%
0.29 1
 
0.1%
0.289 3
 
0.2%
0.288 8
0.6%
0.287 6
0.5%
0.286 3
 
0.2%
0.285 3
 
0.2%

Playoffs
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size19.2 KiB
0
988 
1
244 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1232
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 988
80.2%
1 244
 
19.8%

Length

2023-06-07T11:01:09.535878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-07T11:01:09.678894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 988
80.2%
1 244
 
19.8%

Most occurring characters

ValueCountFrequency (%)
0 988
80.2%
1 244
 
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1232
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 988
80.2%
1 244
 
19.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1232
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 988
80.2%
1 244
 
19.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 988
80.2%
1 244
 
19.8%

RankSeason
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61850649
Minimum0
Maximum8
Zeros988
Zeros (%)80.2%
Negative0
Negative (%)0.0%
Memory size14.4 KiB
2023-06-07T11:01:09.784709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4651927
Coefficient of variation (CV)2.3689205
Kurtosis5.8338518
Mean0.61850649
Median Absolute Deviation (MAD)0
Skewness2.5425474
Sum762
Variance2.1467896
MonotonicityNot monotonic
2023-06-07T11:01:09.909534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 988
80.2%
2 53
 
4.3%
1 52
 
4.2%
4 44
 
3.6%
3 44
 
3.6%
5 21
 
1.7%
6 20
 
1.6%
7 9
 
0.7%
8 1
 
0.1%
ValueCountFrequency (%)
0 988
80.2%
1 52
 
4.2%
2 53
 
4.3%
3 44
 
3.6%
4 44
 
3.6%
5 21
 
1.7%
6 20
 
1.6%
7 9
 
0.7%
8 1
 
0.1%
ValueCountFrequency (%)
8 1
 
0.1%
7 9
 
0.7%
6 20
 
1.6%
5 21
 
1.7%
4 44
 
3.6%
3 44
 
3.6%
2 53
 
4.3%
1 52
 
4.2%
0 988
80.2%

RankPlayoffs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53814935
Minimum0
Maximum5
Zeros988
Zeros (%)80.2%
Negative0
Negative (%)0.0%
Memory size14.4 KiB
2023-06-07T11:01:10.192668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1876042
Coefficient of variation (CV)2.2068301
Kurtosis2.6546884
Mean0.53814935
Median Absolute Deviation (MAD)0
Skewness2.0353726
Sum663
Variance1.4104037
MonotonicityNot monotonic
2023-06-07T11:01:10.324323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 988
80.2%
3 80
 
6.5%
4 68
 
5.5%
2 47
 
3.8%
1 47
 
3.8%
5 2
 
0.2%
ValueCountFrequency (%)
0 988
80.2%
1 47
 
3.8%
2 47
 
3.8%
3 80
 
6.5%
4 68
 
5.5%
5 2
 
0.2%
ValueCountFrequency (%)
5 2
 
0.2%
4 68
 
5.5%
3 80
 
6.5%
2 47
 
3.8%
1 47
 
3.8%
0 988
80.2%

G
Real number (ℝ)

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161.91883
Minimum158
Maximum165
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.2 KiB
2023-06-07T11:01:10.450764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum158
5-th percentile161
Q1162
median162
Q3162
95-th percentile163
Maximum165
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.62436523
Coefficient of variation (CV)0.0038560384
Kurtosis7.0242506
Mean161.91883
Median Absolute Deviation (MAD)0
Skewness-1.0446305
Sum199484
Variance0.38983194
MonotonicityNot monotonic
2023-06-07T11:01:10.571201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
162 954
77.4%
161 139
 
11.3%
163 93
 
7.5%
160 23
 
1.9%
164 10
 
0.8%
159 10
 
0.8%
165 2
 
0.2%
158 1
 
0.1%
ValueCountFrequency (%)
158 1
 
0.1%
159 10
 
0.8%
160 23
 
1.9%
161 139
 
11.3%
162 954
77.4%
163 93
 
7.5%
164 10
 
0.8%
165 2
 
0.2%
ValueCountFrequency (%)
165 2
 
0.2%
164 10
 
0.8%
163 93
 
7.5%
162 954
77.4%
161 139
 
11.3%
160 23
 
1.9%
159 10
 
0.8%
158 1
 
0.1%

OOBP
Real number (ℝ)

Distinct73
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.33226429
Minimum0.294
Maximum0.384
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.2 KiB
2023-06-07T11:01:10.736045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.294
5-th percentile0.31455
Q10.33226429
median0.33226429
Q30.33226429
95-th percentile0.34845
Maximum0.384
Range0.09
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0089235249
Coefficient of variation (CV)0.026856708
Kurtosis4.7727781
Mean0.33226429
Median Absolute Deviation (MAD)0
Skewness0.33445736
Sum409.3496
Variance7.9629297 × 10-5
MonotonicityNot monotonic
2023-06-07T11:01:10.907372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3322642857 812
65.9%
0.329 13
 
1.1%
0.327 13
 
1.1%
0.314 13
 
1.1%
0.336 12
 
1.0%
0.33 12
 
1.0%
0.342 12
 
1.0%
0.319 11
 
0.9%
0.348 11
 
0.9%
0.328 11
 
0.9%
Other values (63) 312
 
25.3%
ValueCountFrequency (%)
0.294 1
 
0.1%
0.296 1
 
0.1%
0.301 1
 
0.1%
0.302 1
 
0.1%
0.303 2
 
0.2%
0.304 1
 
0.1%
0.305 2
 
0.2%
0.306 4
0.3%
0.307 2
 
0.2%
0.308 6
0.5%
ValueCountFrequency (%)
0.384 1
 
0.1%
0.372 1
 
0.1%
0.371 1
 
0.1%
0.369 1
 
0.1%
0.368 1
 
0.1%
0.367 1
 
0.1%
0.365 1
 
0.1%
0.364 1
 
0.1%
0.362 6
0.5%
0.361 2
 
0.2%

OSLG
Real number (ℝ)

Distinct113
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41974286
Minimum0.346
Maximum0.499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.2 KiB
2023-06-07T11:01:11.063360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.346
5-th percentile0.393
Q10.41974286
median0.41974286
Q30.41974286
95-th percentile0.45
Maximum0.499
Range0.153
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.015466119
Coefficient of variation (CV)0.036846652
Kurtosis5.2347689
Mean0.41974286
Median Absolute Deviation (MAD)0
Skewness0.20241509
Sum517.1232
Variance0.00023920084
MonotonicityNot monotonic
2023-06-07T11:01:11.228281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4197428571 812
65.9%
0.431 16
 
1.3%
0.423 13
 
1.1%
0.422 10
 
0.8%
0.398 9
 
0.7%
0.404 8
 
0.6%
0.408 8
 
0.6%
0.407 8
 
0.6%
0.415 8
 
0.6%
0.405 8
 
0.6%
Other values (103) 332
26.9%
ValueCountFrequency (%)
0.346 1
 
0.1%
0.352 1
 
0.1%
0.354 1
 
0.1%
0.361 3
0.2%
0.364 2
0.2%
0.368 1
 
0.1%
0.37 1
 
0.1%
0.371 1
 
0.1%
0.372 3
0.2%
0.373 1
 
0.1%
ValueCountFrequency (%)
0.499 1
 
0.1%
0.494 1
 
0.1%
0.483 1
 
0.1%
0.481 1
 
0.1%
0.48 1
 
0.1%
0.476 4
0.3%
0.475 1
 
0.1%
0.474 1
 
0.1%
0.473 1
 
0.1%
0.471 2
0.2%

Interactions

2023-06-07T11:01:04.152221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:42.260049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:44.384727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:46.361370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:48.288063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:50.479530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:52.211115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:54.501765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:56.437931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:58.647083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:00.508905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:02.350006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:04.295190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:42.687236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:44.574355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:46.524008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:48.610273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:50.614412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:52.415959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:54.672211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:56.653162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:58.828243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:00.704666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:02.484055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:04.445945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:42.829673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:44.746216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:46.673217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:48.753881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:50.746168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:52.596365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:54.825965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:57.063082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:58.999215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:00.846123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:02.614968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:04.589304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:42.984129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:44.911890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:46.823622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:48.932574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:50.885979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:52.758362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:54.985071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:57.210529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:59.172986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:01.001698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:02.753380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:04.742019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:43.137815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:45.061041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:46.999111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:49.109035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:51.023999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:52.949113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:55.151070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:57.354563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:59.321862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:01.150191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:02.894830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:04.889301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:43.278357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:45.223416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:47.141057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:49.268115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:51.147557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:53.176554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:55.320700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:57.491043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:59.451902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:01.292765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:03.018406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:05.050883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:43.454325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:45.373067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:47.298568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:49.443588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:51.301850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:53.361225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:55.491600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:57.647532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:59.606343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:01.454552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:03.176262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:05.223843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:43.622909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:45.566019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:47.482911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:49.616899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:51.446072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:53.524661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:55.650362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:57.814687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:59.763313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:01.609373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:03.381427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:05.538204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:43.785897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:45.723508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:47.656969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:49.858276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:51.589237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:53.688754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:55.813400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:57.970392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:59.922850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:01.763224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:03.549557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:05.676404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:43.930386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:45.873329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:47.799223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:50.012654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:51.718032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:53.848615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:55.957138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:58.108263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:00.063013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:01.909024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:03.718354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:05.825188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:44.082296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:46.033904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:47.947960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:50.159258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:51.902744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:54.008921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:56.109147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:58.252441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:00.223042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:02.062486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:03.873617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:05.963475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:44.223894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:46.178235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:48.117323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:50.305143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:52.054559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:54.197757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:56.249859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:00:58.398976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:00.357951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:02.198314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-07T11:01:04.008717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-07T11:01:11.385186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
YearRSRAWOBPSLGBARankSeasonRankPlayoffsGOOBPOSLGTeamLeaguePlayoffs
Year1.0000.4010.395-0.0030.3390.5320.3140.1630.168-0.008-0.098-0.0740.1060.0000.130
RS0.4011.0000.3450.5030.8840.9120.8060.3790.3890.078-0.0200.0510.1530.1970.388
RA0.3950.3451.000-0.5340.2990.4130.300-0.249-0.252-0.0470.4120.4170.1620.1650.242
W-0.0030.503-0.5341.0000.4680.3900.3940.6130.6250.123-0.381-0.3250.1350.0410.732
OBP0.3390.8840.2990.4681.0000.7680.8330.3500.3590.029-0.0140.0420.1690.1690.371
SLG0.5320.9120.4130.3900.7681.0000.7660.3300.3370.039-0.0150.0390.1790.1540.329
BA0.3140.8060.3000.3940.8330.7661.0000.2880.2900.0420.0250.0750.2080.2170.287
RankSeason0.1630.379-0.2490.6130.3500.3300.2881.0000.9890.032-0.281-0.2470.0950.0090.997
RankPlayoffs0.1680.389-0.2520.6250.3590.3370.2900.9891.0000.028-0.283-0.2510.0800.0000.998
G-0.0080.078-0.0470.1230.0290.0390.0420.0320.0281.000-0.038-0.0110.0000.0980.045
OOBP-0.098-0.0200.412-0.381-0.014-0.0150.025-0.281-0.283-0.0381.0000.7490.1970.0000.310
OSLG-0.0740.0510.417-0.3250.0420.0390.075-0.247-0.251-0.0110.7491.0000.1860.0780.260
Team0.1060.1530.1620.1350.1690.1790.2080.0950.0800.0000.1970.1861.0000.9690.220
League0.0000.1970.1650.0410.1690.1540.2170.0090.0000.0980.0000.0780.9691.0000.000
Playoffs0.1300.3880.2420.7320.3710.3290.2870.9970.9980.0450.3100.2600.2200.0001.000

Missing values

2023-06-07T11:01:06.195992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-07T11:01:06.489253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TeamLeagueYearRSRAWOBPSLGBAPlayoffsRankSeasonRankPlayoffsGOOBPOSLG
0ARINL2012734688810.3280.4180.2590001620.3170.415
1ATLNL2012700600940.3200.3890.2471451620.3060.378
2BALAL2012712705930.3110.4170.2471541620.3150.403
3BOSAL2012734806690.3150.4150.2600001620.3310.428
4CHCNL2012613759610.3020.3780.2400001620.3350.424
5CHWAL2012748676850.3180.4220.2550001620.3190.405
6CINNL2012669588970.3150.4110.2511241620.3050.390
7CLEAL2012667845680.3240.3810.2510001620.3360.430
8COLNL2012758890640.3300.4360.2740001620.3570.470
9DETAL2012726670880.3350.4220.2681621620.3140.402
TeamLeagueYearRSRAWOBPSLGBAPlayoffsRankSeasonRankPlayoffsGOOBPOSLG
1222LADNL19628426971020.3370.4000.2680001650.3322640.419743
1223MINAL1962798713910.3380.4120.2600001630.3322640.419743
1224MLNNL1962730665860.3260.4030.2520001620.3322640.419743
1225NYMNL1962617948400.3180.3610.2400001610.3322640.419743
1226NYYAL1962817680960.3370.4260.2671211620.3322640.419743
1227PHINL1962705759810.3300.3900.2600001610.3322640.419743
1228PITNL1962706626930.3210.3940.2680001610.3322640.419743
1229SFGNL19628786901030.3410.4410.2781121650.3322640.419743
1230STLNL1962774664840.3350.3940.2710001630.3322640.419743
1231WSAAL1962599716600.3080.3730.2500001620.3322640.419743